<p>The design of vehicle components for crashworthiness is one
of the most challenging problems in the automotive industry. The safety of the occupants during a crash
event relies on the energy absorption capability of vehicle structures.
Therefore, the body components of a vehicle are required to be lightweight and
highly integrated structures. Moreover, reducing vehicle weight is another
crucial design requirement since fuel economy is directly related to the mass
of a vehicle. In order to address these requirements, various design concepts
for vehicle bodies have been proposed using high-strength steel and different
aluminum alloys. However, the price factor has always been an obstacle to
completely replace regular body steels with more advanced alloys. To this end,
the integration of numerical simulation and structural optimization techniques
has been widely practiced addressing these requirements. Advancements in
nonlinear structural design have shown the promising potential to generate
innovative, safe, and lightweight vehicle structures. In addition, the
implementation of structural optimization techniques has the capability to
shorten the design cycle time for new models. A reduced design cycle time can
provide the automakers with an opportunity to stay ahead of their competitors. During the last few decades, enormous
structural optimization methods were proposed. A vast majority of these methods
use mathematical programming for optimization, a method that relies on
availability sensitivity analysis of objective functions. Thus, due to the necessity of sensitivity
analyses, these methods remain limited to linear (or partially nonlinear)
material models under static loading conditions. In other words, these methods
are no able to capture all non-linearities involved in multi-body crash
simulation. As an alternative solution,
heuristic approaches, which do need sensitivity analyses, have been developed
to address structural optimization problems for crashworthiness. The Hybrid
Cellular Automaton (HCA), as a bio-inspired algorithm, is a well-practiced
heuristic method that has shown promising capabilities in the structural design
for vehicle components. The HCA has been
continuously developed during the last two decades and designated to solve
specific structural design applications.
Despite all advancements, some fundamental aspects of the algorithm are
still not adequately addressed in the literature. For instance, the HCA
numerically implemented as a closed-loop control system. The local controllers,
which dictate the design variable updates, need parameter tuning to efficiently
solve different sets of problems.
Previous studies suggest that one can identify some default values for
the controllers. However, still, there is no well-organized strategy to tune
these parameters, and proper tuning still relies on the designer’s experience.</p>
<p> </p>
<p> Moreover, structures
with multiple materials have now become one of the perceived necessities for
the automotive industry to address vehicle design requirements such as weight,
safety, and cost. However, structural design methods for crashworthiness,
including the HCA, are mainly applied to binary structural design problems.
Furthermore, the conventional methods for the design of multi-material
structures do not fully utilize the capabilities of premium materials. In other
words, the development of a well-established method for the design of
multi-material structures and capable of considering the cost of the materials,
bonding between different materials (especially categorical materials), and manufacturing
considering is still an open problem. Lastly, the HCA algorithm relies only on
one hyper-parameter, the mass fraction, to synthesize structures. For a given problem, the HCA only provides
one design option directed by the mass constraint. In other words, the HCA
cannot tailor the dynamic response of the structure, namely, intrusion and
deceleration profiles.</p>
<p> </p>
<p>The main objective of this dissertation is to develop new
methodologies to design structures for crashworthiness applications. These
methods are built upon the HCA algorithm. The first contribution is about
introducing s self-tuning scheme for the controller of the algorithm. The
proposed strategy eliminates the need to manually tune the controller for
different problems and improve the computational performance and numerical
stability. The second contribution of this dissertation is to develop a
systematic approach to design multi-material crashworthy structures. To this
end, the HCA algorithm is integrated with an ordered multi-material SIMP (Solid
Isotropic Material with Penalization) interpolation. The proposed
multi-material HCA (MMHCA) framework is a computationally efficient method
since no additional design variables are introduced. The MMHCA can synthesize
multi-material structures subjected to volume fraction constraints. In
addition, an elemental bonding method is introduced to simulate the laser
welding applied to multi-material structures. The effect of the bonding
strength on the final topology designs is studied using numerical simulations.
In the last step, after obtaining the multi-material designs, the HCA is
implemented to remove the desired number of bonding elements and reduce the
weld length.</p>
<p> </p>
<p>The third contribution of this dissertation is to introduce
a new Cluster-based Structural Optimization method (CBSO) for the design of
multi-material structures. This contribution introduces a new Cluster Validity
Index with manufacturing considerations referred to as CVI<sub>m</sub>. The proposed index can characterize the quality of
the cluster in structural design considering volume fraction, size, interface
as a measure of manufacturability. This multi-material structural design
approach comprises three main steps: generating the conceptual design using adaptive
HCA algorithm, clustering of the design domain using Multi-objective Genetic
Algorithm (MOGA) optimization. In the third step, MOGA optimization is used to
choose categorical materials in order to optimize the crash indicators (e.g.,
peak intrusion, peak contact force, load uniformity) or the cost of the raw
materials. The effectiveness of the algorithm is investigated using numerical
examples.</p>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/15079062 |
| Date | 30 July 2021 |
| Creators | Sajjad Raeisi (11205861) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/thesis/DESIGN_OF_MULTI-MATERIAL_STRUCTURES_FOR_CRASHWORTHINESS_USING_HYBRID_CELLULAR_AUTOMATON/15079062 |
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